Objectives for Responsible Development of AI Systems
Write down the responsible-development goals your AI systems must meet, then build checkpoints into each stage of development so those goals actually shape what gets built and released.
Plain language
This control asks you to decide, up front, what "responsible" means for the AI you build (for example that it must be accurate, fair across different groups of people, explainable, and safe) and to write those goals down. It then asks you to weave those goals into the way you actually build the system, from the first design sketch through to the moment someone signs off the release, so the goals are not just a poster on the wall. The aim is that a developer cannot quietly skip them, because each stage of building the AI has to show it has met the goals before work moves on.
Framework
ISO/IEC 42001:2023
Control effect
Preventative
Classifications
N/A
Official last update
01 Dec 2023
Control Stack last updated
19 June 2026
Official control statement
The organisation shall identify and document objectives to guide the responsible development AI systems, and take those objectives into account and integrate measures to achieve them in the development life cycle.
Why it matters
Without documented responsible-development objectives wired into the build, a recruitment-screening model can reach production having been trained on years of biased hiring data with no fairness goal ever set or tested, so it systematically down-ranks women and older applicants, the kind of automated decision that draws an OAIC complaint or a discrimination claim once a rejected cohort works out why they keep being filtered. Teams also end up reverse-engineering goals like explainability or human-override after launch, which usually means a costly rebuild or pulling the system. When the objective was never written down, there is also nothing to test against, so no one can prove the AI was fit to release.
Operational notes
Keep the objectives in a single living document tied to each AI system, and revisit them whenever the system's purpose, training data source, or user population changes, not only at an annual review. Make the development-stage gates real by recording, against each objective, the evidence that satisfied it (a fairness test result, an explainability review, a sign-off) before the system moves from one stage to the next. When a project genuinely cannot meet an objective, record the deviation and who accepted it rather than letting the gate be skipped silently, so the trade-off is visible later.
Implementation tips
- Have the AI product owner draft a short, specific list of responsible-development objectives for each AI system (things like a minimum accuracy level, fairness across the groups the system affects, explainability of its decisions, security, and where a human must stay in the loop) and get a named accountable owner to approve it so the goals are settled before coding starts.
- Get the development lead to build those objectives into your stage-gate or definition-of-done so each phase of the life cycle has to satisfy them: the design stage records how the architecture will meet explainability and oversight, the data-sourcing stage checks training data against the fairness objective, the training and testing stages produce results measured against the accuracy and bias targets, and release is blocked until each objective is confirmed.
- Askthe data and model engineers to translate each objective into a concrete check they run during the build: for example a bias test across the named groups before a model is accepted, or a documented data-provenance record (where the training data came from and that it was lawful to use) that satisfies a data-quality objective
- Have the test or QA lead produce evidence against the objectives before release (a fairness report, an accuracy benchmark, an explainability review) and attach it to the release sign-off so no system ships without showing it met the goals.
- When a project cannot meet an objective in the planned timeframe, have the accountable owner record the deviation, the reason, and a remediation date rather than dropping the objective, so the gate stays meaningful and the trade-off is on the record.
Audit / evidence tips
- AskAsk the AI system owner for the documented responsible-development objectives for a specific AI system.GoodThere is a dated, approved list of responsible-development objectives specific to the AI system, covering qualities such as accuracy, fairness, explainability, safety and human oversight.
- AskAsk for the development life-cycle or stage-gate checklist that ties each objective to a development stage.GoodEach objective is assigned to a development stage and the checklist shows it was verified, with evidence, before work moved on.
- AskAsk to see the test or evaluation results produced before the system was released.GoodPre-release test results map directly to the documented objectives and show each was assessed before sign-off.
- AskAsk for the release sign-off record for the AI system.GoodThe sign-off confirms each objective was met, or records a deviation that a named approver formally accepted.
- AskAsk the development team to walk through how an objective changed a build decision.GoodThe team can name a concrete build decision (such as choosing an explainable model or rebalancing training data) that an objective directly drove.
Cross-framework mappings
How Annex A 6.1.2 relates to controls across ISO/IEC 27001, ISO/IEC 42001, Essential Eight, and ASD ISM.
ASD ISM
| Control | Notes | Details |
|---|---|---|
handshakeSupports(3)expand_less | ||
| ISM-0041 | Annex A 6.1.2 requires the organisation to identify and document responsible AI development objectives and integrate measures to achieve ... | |
| ISM-1998 | Annex A 6.1.2 requires defined responsible AI development objectives and their integration into development activities | |
| ISM-1999 | Annex A 6.1.2 requires the organisation to identify, document, and integrate objectives for responsible AI development into the AI develo... | |
These mappings show relationships between controls across frameworks. They do not imply full equivalence or certification.
Related ISO 42001 controls in A.6 AI system life cycle
See all A.6 AI system life cycle controls, or browse the full ISO/IEC 42001:2023 library.
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